Facilitating the automatic capturing of high quality images with little or no user intervention is the primary objective in any consumer level digital or cell-phone camera system design. Consumers often rank image sharpness as a major factor of final image quality [1]. The sharpness of the final captured image is determined in large part by the ability of the camera's auto-focus (AF) system to adjust the distance between the lens and image sensor in such a way as to render an object of interest at a certain distance from the camera onto the image sensor with an imperceptible amount of blur.
Due to its ease of hardware integration, the passive AF approach has become the defacto standard in realizing the AF feature for compact consumer digital or cell-phone camera systems. In passive AF, a measure of image sharpness is extracted from a portion of the captured image. This measure is then used to adjust the imaging distance via a search algorithm running on the camera's processor so that the extracted measure obtains a peak value [2].
There are three components to any passive AF method namely: focus region, sharpness measure, and sharpness search algorithm [3]. As far as sharpness search algorithms are concerned, several attempts have been made to determine the in-focus position quickly without overshooting or oscillating around the peak as consumers desire a smooth AF experience with minimum overshoot and no rapid oscillation between extreme levels of focus and defocus [4], [5]. The efficiency of the search depends on the number of times the distance between the lens and image sensor is adjusted to bring the image into focus.
Many different types of search approaches can be found in the literature including Global Search, Iterative Coarse-to-Fine Search, Divide-and-Conquer Search, Prediction Search, and Variable Step-Size Sequential Search. The Global Search (GS) approach sequentially searches every position in the search space, and uses the position of maximum sharpness as the in-focus position. A GS-found in-focus position provides the true in-focus position and thus can be used to compare the accuracy of other search methods. Iterative coarse-to-fine approaches include Choi's fast hopping search [6], which combines a coarse scan with GS near the estimated peak location, and Li's fixed step-size coarse search [7], which uses a fine scan near the estimated coarse peak. The divide-and-conquer approach is exemplified with the Fibbonaci [8] search. Although this search is optimal in minimizing the number of iterations for a given search space, it is not a viable method for a consumer camera AF system due to the oscillation around the peak and its inefficiency in total distance moved [4], [5]. In [9], Chen et al. presented a prediction search to forecast the turning point of the sharpness measure which helped to reduce the number of iterations. Several variable step-size search methods have been proposed for adjusting the speed of search, the key differentiator in such methods lies in how to determine the step-size. Fuzzy rule-based [10], [11] and crisp rule-based [3], [12] methods have been applied to adjust the step-size in a heuristic manner, while in [13], Batten adapted the step-size in order to keep the gradient of the sharpness measure constant. In [14], Yao used a Maximum-Likelihood statistical approach to determine thresholds in a crisp rule-based type search for the adjustment of the step-size.
In [15], Yao et al. presented a study of sharpness measures and search algorithms, where it was found that the variable step-size Rule-based Search (RS) [12] outperformed other search algorithms in terms of accuracy and convergence speed. In other words, it has been independently verified that such a sequential search is able to reduce the number of iterations and to eliminate the oscillation found in the divide-and-conquer approaches. In [3], the effectiveness of the RS approach was also confirmed and improvements were made to it, named Modified-RS (MRS), that achieved better performance than the original RS.
The existing sharpness search algorithms presented in the literature, including RS and MRS, are performed based on user-defined thresholds, rules, and step-size magnitudes. However, since the sharpness measure depends on many factors including the optics, the sensor, the object of interest and its surrounding environment, it becomes difficult to tune such thresholds to control the adjustment of the step-size to achieve an efficient search across different camera platforms.
Furthermore, in the digital band-pass filter sharpness measure method that is popular with camera manufacturers, the choice of filter pass-band is somewhat arbitrary. Only general recommendations have been mentioned in the literature such as using a low-frequency band at the initial searching stage and then using a higher-frequency band to accurately locate the peak [16]. It is not mentioned how the pass-band should be changed when changing the camera optics and sensor.
Thus, from a camera manufacturer's point of view, it is desirable to parameterize the passive AF system so that it becomes portable across many different camera platforms. The existing AF algorithms, however, do not adequately address how to properly set the AF parameters, such as the filter pass-band and the step-size magnitude. Accordingly, there is a need for AF method and system that automatically derives the passive AF parameters, reduces the number of iterations and lowers the auto focusing time while not compromising sharpness quality.